Abstract
Early and precise diagnosis of bronchopulmonary dysplasia (BPD) is essential to improve the prognosis of preterm infants with BPD. Studying ferroptosis-related genes for diagnostic markers of BPD was the objective of this study. Using the GEO database and the FerrDb database, we obtained the GSE32472 dataset and screened the ferroptosis-related differentially expressed mRNAs (FRDE-mRNAs). By using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), possible biological functions and pathways were identified for FRDE-mRNAs. Three machine learning algorithms (LASSO, SVM-RFE, Random Forest) were used to recognize hub genes, as well as CIBERSORT for exploring the immune landscape of BPD and controls. Functional predictions for hub genes were made using single-gene gene set enrichment analysis (GSEA). Twenty three FRDE-mRNAs were obtained and were mainly involved in autophagy, fatty acid metabolism and ferroptosis. The four hub genes (LPIN1, ACADSB, WIPI1 and SLC7A11) screened were utilized to construct a diagnostic nomogram. The receiver operating characteristic (ROC) curves and calibration curves demonstrateld that the nomogram exhibited good predictive performance. Eight types of immune cell markers differed significantly between BPD and controls. We developed a diagnostic model for BPD, which could facilitate the early diagnosis and timely intervention of BPD. The role of ferroptosis in bronchopulmonary dysplasia is rarely reported. The ferroptosis-related genes (LPIN1, ACADSB, WIPI1 and SLC7A11) we identified could serve as early diagnostic biomarkers for BPD. Immune cell infiltration features in BPD and signaling pathways associated with marker genes give new insight into the disease process and provide a basis for further research.
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